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image description Luv

Software Developer in Ottawa

Location:Ottawa, ON, Canada (Anywhere)

Education level: College/University
Education field: Engineering
Years of experience: 2 - 5 years
Rate: 20
Attributes that best describes:
Team player
Strong analytical skills
Organised
Pragmatic
Logical

Skills:
ONLINE COURSES
- AWS Fundamentals: Going Cloud-Native (Coursera), Building Serverless Applications (Coursera)
- Google Cloud Platform: Big Data and Machine Learning Fundamentals (Coursera), Getting Started With Google Kubernetes Engine (Coursera)

CERTIFICATIONS
- AWS Training & Certification – Machine Learning Data Readiness, Elements of Data Science, Data Analytics Fundamentals, Amazon Database Offerings
- Coursera Specialization – Machine Learning with TensorFlow on Google Cloud Platform
SKILLS & ABILITIES

MANAGEMENT
- Able to manage difficult situations with precision
- Organized and professional
- Works well independently and with others
- A mature sense of work etiquette

ANALYTICAL SKILLS
- Proven ability to critically evaluate any given situation
- Experienced in research and analysis
- Proficient in operating the Microsoft Office software for various projects
- A keen eye for detail

COMMUNICATION
- Proven ability to communicate well with people from many diverse ranges of backgrounds.
- Managing information that circulates internally and externally
- Spoken Languages – English (Fluent), Hindi (Fluent), French (Beginner)
- Exceptional comprehension of keeping records and logs

SOFTWARE SKILLS & LANGUAGES
- SCRIPTING – C, C++, C#, MATLAB, PYTHON, R, JAVA
- WEB DESIGN - HTML, JAVASCRIPT (NODEJS), TYPESCRIPT (ANGULARJS), REACT, JQUERY, PHP, CSS, REST/WEB APIS, ADOBE PHOTOSHOP
- WEB SCRAPING
- DATA FRAMES/LIBRARIES – TENSORFLOW, NUMPY, PANDAS, SCIKIT-LEARN
- DATA / DATABASES – JSON, XML, MYSQL, MONGODB, SQL SERVER, ORACLE, GRAPHQL
- WEB FRAMEWORKS – FLASK, ASP.NET, MVC, RUBY
- Simulink, BlueJ, Netbeans, Visual Studio 2017, Photoshop, IIS, Azure Services, RStudio, Google Cloud Platform, Jupyter Notebook, GitHub, SQL Server Management Studio, Kubernetes, Docker, Apache Hadoop, Agile Methodologies, UML
- AWS - S3, EC2 Instances, VPCs, API Gateways, Lambda, Lex, SageMaker, Neptune, Redshift, DocumentDB, ElastiCache
- Other Web Service Platforms – Cpanel, Plesk

PART-TIME WEB DEVELOPER | HOUSEBUILDERREVIEWS.COM (VIA FREELANCER.COM) | JULY 2019 – PRESENT
- I have developed an ASP.NET web application to allow home owners and renters to enter property review surveys and storing the entered data in an SQL Server database for future home seekers to view, along with an admin interface to access and use the stored data.
- It generates leads via review data and internet cookies and monitors activity on the website in general.
- Have implemented various web APIs, like Google Maps, Gmail, SendGrid, etc., via HTTP requests and similar API calls.
- Developing automated Excel document reports of different builders based on individual scores from property reviews, location, etc.
- Initially deployed both the website design and script files via Visual Studio, and the database via SQL Server Management Studio, on Azure.
- Have migrated all of the website design and script files and database from Azure to Plesk for web hosting.
- Currently am working on SEO for the website (e.g., logo design with Photoshop).
- Have installed Facebook pixel and Google Analytics JS code into the web pages to track activity on the website via Facebook ads and general visits to the website.

EUROPEAN FOOTBALL SEER WEB APP (REACT APPLICATION) | DECEMBER 2018 – PRESENT
- The objective of this application is to aid sports betting by calculating probabilities of wins/losses/draws for upcoming match fixtures between any pair of European football teams.
- Have developed Python code to scrape sufficient recent statistics and scheduling information via Selenium Chromedriver from sports websites, like WhoScored.com, to calculate the current form of players on both teams, and store the data in an MySQL database and in JSON format.
- Currently writing more Python code to use machine learning to develop the best prediction model for every player of a given team “succeeding” against the entire opposing team, based on statistics gathered on both team’s most recent performances (e.g., number of goals, number of successful tackles, etc.)
- The probability of a win / draw / loss will then be mathematically calculated based on the individual player probabilities for successful performance against the opposing and individual team members on aggregate.
- Currently am developing a React application for an end-user to implement for match predictions, along with significant insights into key details to look out for during an upcoming match, e.g., key battle moments between a striker and central defender.
- Currently am scheduling Python Selenium Chromedriver tasks via AWS Lambda to regularly scrape the latest match statistics that will eventually be used for predictions, and store the gained statistics within an S3 bucket.
- Via SCP and an EC2 instance, I have migrated the MySQL database to Amazon RDS.

Languages: English, Hindi

 

 
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